Thanks for the very reasonable question. In short, our current budget for the next financial year (through to June 2026) is currently earmarked to existing programmatic obligations. Additional marginal funding would allow us to bring in support to start building out this solution and we would leverage existing team members for the on-ground validation exercises. 

Thank you for the question — we recently presented our validation results at the Nutrition Society of India Conference, and you can view the full slide deck here: https://docs.google.com/presentation/d/1ReSC7R1HmV9_i61nu12YX5rOV56S6hGY_B3etFP5p3E/edit?usp=sharing. Our computer vision model, trained on 837 iron spot test images, currently achieves ~84% accuracy on test data and substantially reduces the subjectivity seen in manual qualitative testing. While it doesn’t replace quantitative lab methods like ICP-MS, it provides a low-cost, reliable, mill-level QA tool to flag potential under- or over-fortification more consistently.